Intelligent Infrastructure and Construction

This study aims to explore the transformative potential of Artificial Intelligence (AI) in enhancing cost planning and control within Qatar’s construction industry. By examining both opportunities and challenges associated with the adoption of AI, it seeks to uncover that AI can lead to significant improvements in accuracy in cost estimates and optimisation of various resources. The nation faces significant cost-overruns influenced by delays, shifting market conditions, and although AI has demonstrated its benefits in cost-control management globally, there is a lack of research on its practical applications in Qatar’s construction industry. Existing practical applications are more likely to experience errors due to them requiring manual labour and limited pattern recognition. Meanwhile, this study attempts to align AI-driven advancements with Qatar’s Vision 2030, which emphasises sustainable development and economic diversification. It adopts an analysis of semi-structured interviews with a group of experienced professionals from leading construction companies in Qatar, giving a comprehensive picture of the current landscape and future prospect for AI in the construction industry. The findings of this study reveal that AI technologies can significantly mitigate common issues in the construction industry, such as cost overruns, project delays, and resource wastage. On the other hand, this study identifies various obstacles that inhibit AI adoption, including high financial costs and insufficient training data. By weaving together theoretical understandings and practical experiences, it highlights the importance of integrating AI technologies within existing workflows while addressing key concerns.

Tunnels serve as a critical hub in urban transportation networks; their monotonous and enclosed environment is prone to inducing speeding behavior, necessitating an efficient vehicle speed monitoring system. Traditional methods suffer from high costs and slow response times, making them inadequate for the complex scenarios encountered in tunnel environments. This study proposes a real-time tunnel vehicle speed monitoring system based on YOLOv8s and DeepSORT. YOLOv8s is used to detect and classify cars, trucks, and buses, while DeepSORT applies Kalman filtering and the Hungarian algorithm to construct motion trajectories. Vehicle speed is estimated through perspective geometric transformation combined with a sliding-window approach, with a speeding threshold of 100 km/h and corresponding visual alerts. Using surveillance video from an expressway tunnel as the dataset, the system achieved detection accuracies of 98% for cars, 96% for trucks, and 91% for buses. Speed detection performance metrics included an average speed deviation (ASD) of 2.54 km/h, a deviation degree of vehicle speed (DDVS) of 3.12, vehicle speed stability (VST) of 1.22, and speed difference ratio (SDR) of 2.9%. Analysis revealed a longitudinal “deceleration–acceleration–deceleration” inverted U-shaped speed profile along the tunnel. Statistical tests confirmed these findings: the Mann–Whitney U test showed highly significant differences in vehicle speeds between cars and trucks across different tunnel sections, and the Kruskal–Wallis test further indicated significant speed variations across the entrance, middle, and exit segments for both vehicle types.

This study presents a nonlinear regression expansion model tailored to the characteristics of fissured highly expansive soils. Through in-depth investigations, fissure ratio (K_r), dry density (ρ_d), initial water content (w₀), and overburden stress (ln(1 + σ)) were identified as critical factors influencing expansion behavior. Experimental results revealed linear relationships between ultimate expansion (δ_ep) and w₀, ρ_d, and ln(1 + σ), and an exponential relationship with K_r. A multivariate nonlinear regression model was developed and validated, demonstrating high predictive accuracy. The model highlights the significant role of fissure infill materials, particularly gray-green clay, on soil expansiveness. It provides a reliable tool for predicting the expansion characteristics of fissured expansive soils under various conditions, offering theoretical and practical support for engineering applications in expansive soil regions. This study uses a single highly expansive clay from the Nanyang section. The soil is a transported Middle Pleistocene alluvial–proluvial clay (al-plQ2) in which fissures are predominantly filled by 2–5 mm gray-green clay. Accordingly, the proposed regression is most applicable to fissure systems that are largely infilled; extrapolation to open or partially infilled fissures should be made with caution.